LLM-DSE: Searching Accelerator Parameters with LLM Agents
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909915779432448 |
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| author | Wang, Hanyu Wu, Xinrui Ding, Zijian Zheng, Su Wang, Chengyue Prakriya, Neha Nowatzki, Tony Sun, Yizhou Cong, Jason |
| author_facet | Wang, Hanyu Wu, Xinrui Ding, Zijian Zheng, Su Wang, Chengyue Prakriya, Neha Nowatzki, Tony Sun, Yizhou Cong, Jason |
| contents | Even though high-level synthesis (HLS) tools mitigate the challenges of programming domain-specific accelerators (DSAs) by raising the abstraction level, optimizing hardware directive parameters remains a significant hurdle. Existing heuristic and learning-based methods struggle with adaptability and sample efficiency. We present LLM-DSE, a multi-agent framework designed specifically for optimizing HLS directives. Combining LLM with design space exploration (DSE), our explorer coordinates four agents: Router, Specialists, Arbitrator, and Critic. These multi-agent components interact with various tools to accelerate the optimization process. LLM-DSE leverages essential domain knowledge to identify efficient parameter combinations while maintaining adaptability through verbal learning from online interactions. Evaluations on the HLSyn dataset demonstrate that LLM-DSE achieves substantial $2.55\times$ performance gains over state-of-the-art methods, uncovering novel designs while reducing runtime. Ablation studies validate the effectiveness and necessity of the proposed agent interactions. Our code is open-sourced here: https://github.com/Nozidoali/LLM-DSE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12188 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-DSE: Searching Accelerator Parameters with LLM Agents Wang, Hanyu Wu, Xinrui Ding, Zijian Zheng, Su Wang, Chengyue Prakriya, Neha Nowatzki, Tony Sun, Yizhou Cong, Jason Hardware Architecture Artificial Intelligence Even though high-level synthesis (HLS) tools mitigate the challenges of programming domain-specific accelerators (DSAs) by raising the abstraction level, optimizing hardware directive parameters remains a significant hurdle. Existing heuristic and learning-based methods struggle with adaptability and sample efficiency. We present LLM-DSE, a multi-agent framework designed specifically for optimizing HLS directives. Combining LLM with design space exploration (DSE), our explorer coordinates four agents: Router, Specialists, Arbitrator, and Critic. These multi-agent components interact with various tools to accelerate the optimization process. LLM-DSE leverages essential domain knowledge to identify efficient parameter combinations while maintaining adaptability through verbal learning from online interactions. Evaluations on the HLSyn dataset demonstrate that LLM-DSE achieves substantial $2.55\times$ performance gains over state-of-the-art methods, uncovering novel designs while reducing runtime. Ablation studies validate the effectiveness and necessity of the proposed agent interactions. Our code is open-sourced here: https://github.com/Nozidoali/LLM-DSE. |
| title | LLM-DSE: Searching Accelerator Parameters with LLM Agents |
| topic | Hardware Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2505.12188 |